Biomedical subjects

Martin W McIntosh

Publications and source records attributed to Martin W McIntosh.

15 recordsLinked to original sources

PubMed2006

Modification of host lipid raft proteome upon hepatitis C virus replication.

Hepatitis C virus (HCV) replication complex resides in detergent-insoluble subcellular domains or lipid rafts. We used two proteomics approaches to characterize the protein content of lipid rafts isolated from Huh7 cells and its modification upon HCV replication. Using two-dimensional electrophoresis and mass spectrometry, we identified approximately 100 protein spots in the isolated lipid rafts; among them, 39 were reproducibly modified in HCV replicon cell lines as compared with control cell lines. We also used stable isotope labeling by amino acids in cell culture (SILAC) combined with one-dimensional electrophoresis separation and mass spectrometry. Using this approach, we identified 1036 individual proteins based on peptides selected with at least 95% confidence; among them, 413 proteins were identified with at least two peptides. Quantification analysis identified 150 proteins modified by at least 2.5-fold (110 up-regulated and 40 down-regulated) in HCV-replicating cells compared with controls. Protein identifications and quantifications obtained by both proteomics approaches were largely concordant. Modulated proteins included a majority of proteins involved in vesicular and protein trafficking and in cell signaling. Remarkably for a large number of proteins, their up-regulation in lipid rafts of HCV replicon cells was due to their relocalization. By using small interfering RNAs directed to the modulated small GTPases Cdc42 and RhoA, we observed an increase in HCV replication, whereas reduction of syntaxin 7 expression resulted in decreased replication of HCV. Our findings indicate that protein subcellular relocalization occurs in HCV-containing cells that can directly affect HCV replication.

Cell Extracts↗

PubMed2006

Antibody immunity to the p53 oncogenic protein is a prognostic indicator in ovarian cancer.

PURPOSE: Presence of intratumoral T-cell infiltration has been linked to improved survival in ovarian cancer patients. We questioned whether antibody immunity specific for ovarian cancer tumor antigens would predict disease outcome. We evaluated humoral immune responses against ovarian cancer antigens p53, HER-2/neu, and topoisomerase IIalpha. PATIENTS AND METHODS: Serum was collected from 104 women (median age, 59 years; range, 34 to 89 years) at the time of their initial definitive surgery for ovarian cancer. Serum was analyzed by enzyme-linked immunosorbent assay for antibodies to p53, HER-2/neu, and topoisomerase IIalpha proteins. Antibody immunity to tetanus toxoid was assessed as a control. The incidence of humoral immunity at the time of diagnosis to any of these three antigens was tabulated. For patients with advanced-stage disease (III/IV), correlation was made between the presence of tumor-specific immunity at the time of diagnosis and overall survival. Patients were followed for a median of 1.8 years. RESULTS: Multivariate analysis showed the presence of p53 antibodies to be an independent variable for prediction of overall survival in advanced-stage patients. Overall survival was significantly higher for patients with antibodies to p53 when compared with patients without p53 antibodies (P = .01). The median survival for p53 antibody-positive patients was 51 months (95% CI, 23.5 to 60.5 months) compared with 24 months (95% CI, 19.4 to 28.6 months) for patients without antibodies to p53. CONCLUSION: Data presented here demonstrate that advanced stage ovarian cancer patients can have detectable tumor-specific antibody immunity and that immunity to p53 may predict improved overall survival in patients with advanced-stage disease.

Adult↗

PubMed2006

Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments.

The open-source Computational Proteomics Analysis System (CPAS) contains an entire data analysis and management pipeline for Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) proteomics, including experiment annotation, protein database searching and sequence management, and mining LC-MS/MS peptide and protein identifications. CPAS architecture and features, such as a general experiment annotation component, installation software, and data security management, make it useful for collaborative projects across geographical locations and for proteomics laboratories without substantial computational support.

Computational Biology↗

PubMed2006

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.

Algorithms↗

PubMed2006

Evaluation of 12 antibodies for distinguishing epithelioid mesothelioma from adenocarcinoma: identification of a three-antibody immunohistochemical panel with maximal sensitivity and specificity.

We evaluated the sensitivity and specificity of 10 monoclonal and two polyclonal antibodies for distinguishing epithelioid mesothelioma from adenocarcinoma (AdCA) using immunohistochemistry (IHC). The antibodies were directed against the mesothelial-associated antigens mesothelin, calretinin, cytokeratin 5, thrombomodulin, Wilms' tumor-1 (WT-1) gene product and HBME-1, and the nonmesothelial antigens Lewis-Y blood group (antibody BG8), MOC-31, BerEp4, CD15, and carcinoembryonic antigen (CEA) family. The 133 tumors evaluated included 65 malignant epithelioid mesotheliomas, 22 lung AdCAs, 27 ovarian serous carcinomas, 24 breast carcinomas, and five gastric carcinomas. Diagnoses were based on clinical, histologic, ultrastructural, and/or IHC findings. Calretinin had the best sensitivity for mesothelioma (95%), followed by HBME-1 (84%), WT-1 (78%), cytokeratin 5 (76%), mesothelin (75%), and vimentin and thrombomodulin (68%). Thrombomodulin had the best specificity for mesothelioma (92%), followed by cytokeratin 5 (89%), calretinin (87%) vimentin (84%), and HBME-1 (45%). When ovarian carcinomas were excluded from the analysis, the specificity of mesothelin and WT-1 for the diagnosis of mesothelioma increased to 90 and 81%, respectively. The sensitivity of the nonmesothelial antigens for AdCA was organ dependent, with BG8 performing best in the breast cancer group (96%), and BerEp4, BG8, MOC-31 performing best in the lung cancer group (100%). The specificity of the nonmesothelial antigens for AdCA was 98% for BG8 and CEA, 97% for CD15, 95% for BerEp4, and 87% for MOC-31. A novel statistical analysis technique employing logic regression analysis identified a three-antibody immunohistochemical panel including calretinin, BG8, and MOC-31, which provided over 96% sensitivity and specificity for distinguishing epithelioid mesothelioma from AdCA.

Adenocarcinoma↗

PubMed2006

Comparing adaptive and non-adaptive algorithms for cancer early detection with novel biomarkers.

It may be possible to reduce cancer mortality by monitoring the concentrations of serum biomarkers over time in men and women to detect their cancer early, when it is most curable. The simplest approach to using a biomarker for screening is to sequentially use fixed thresholds as a means to determine an abnormal test (e.g., PSA exceeding 4 mg/ml, CA 125 exceeding 30 U/ml). Alternatives to the simplest single threshold (ST) rules include more sophisticated algorithms that make use of screening history that accumulates over time and determines abnormal tests using individualized reference ranges. Although in principle longitudinal algorithms should out perform fixed threshold rules, the actual benefit gained will depend on behavior of the biomarker, the screening algorithm, and the screening frequency. Little information has been available to help predict when conditions should compel the adoption of the more sophisticated algorithms and when conditions suggest the simpler algorithms should suffice, or indeed be preferred. In this manuscript we evaluate the conditions under which one should expect great benefit, and when one should not expect benefit, by comparing the ability of simple and complex algorithms to detect cancer early under a variety of biomarker behaviors and screening frequencies.

Algorithms↗

PubMed2005

Detection of hypermethylated genes in women with and without cervical neoplasia.

BACKGROUND: DNA methylation changes are an early event in carcinogenesis and are often present in the precursor lesions of various cancers. We examined whether DNA methylation changes might be used as markers of cervical intraepithelial neoplasia (CIN) and invasive cervical cancer (ICC). METHODS: We used methylation-specific polymerase chain reaction (PCR) to analyze promoter hypermethylation of 20 genes, selected on the basis of their role in cervical cancer, in 319 exfoliated cell samples and matched tissue biopsy specimens collected during two studies of Senegalese women with increasingly severe CIN and ICC (histology negative/atypical squamous cells of undetermined significance [ASCUS] = 142, CIN-1 = 39, CIN-2 = 23, CIN-3/carcinoma in situ [CIS] = 23, ICC = 92). Logic regression was used to determine the best set of candidate genes to use as disease markers. All statistical tests were two-sided. RESULTS: Similar promoter methylation patterns were seen in genes from exfoliated cell samples and corresponding biopsy specimens. For four genes (CDH13, DAPK1, RARB, and TWIST1), the frequency of hypermethylation increased statistically significantly with increasing severity of neoplasia present in the cervical biopsy (P<.001 for each). By using logic regression, we determined that the best panel of hypermethylated genes included DAPK1, RARB, or TWIST1. At least one of the three genes was hypermethylated in 57% of samples with CIN-3/CIS and in 74% of samples with ICC but in only 5% of samples with CIN-1 or less. The estimated specificity of the three-gene panel was 95%, and its sensitivity was 74% (95% confidence interval [CI] = 73% to 75%) for ICC and 52% (95% CI = 49% to 55%) for CIN-3/CIS. By extrapolation, we estimated that, among Senegalese women presenting to community-based clinics, detection of the DAPK1, RARB, or TWIST1 hypermethylated gene would reveal histologically confirmed CIN-3 or worse with a sensitivity of 60% (95% CI = 57% to 63%) and a specificity of 95% (95% CI = 94% to 95%). CONCLUSIONS: Aberrant promoter methylation analysis on exfoliated cell samples is a potential diagnostic tool for cervical cancer screening that potentially may be used alone or in conjunction with cytology and/or human papillomavirus testing.

Adult↗

PubMed2004

Characteristics and presenting complaints of outpatients with undiagnosed HIV infection: potential utility in selecting subjects for HIV testing.

HIV testing of individuals presenting to outpatient medical clinics has generally been based upon a selection system, with testing limited to those having signs or symptoms previously found associated with HIV-1 infection among hospitalized patients. However, little is known about the efficacy of this approach, particularly in Africa. Among patients presenting to a large outpatient infectious disease clinic in Dakar, Senegal, the utility of using specific demographic and behavioral characteristics and individual presenting complaints to identify individuals with previously undiagnosed HIV-1 or HIV-2 infection was examined. Using a simple statistical approach, a composite screening rule was estimated to identify subjects with the highest probability of testing HIV positive, ie, patients who would most benefit from HIV testing. Using the presenting complaint allows identification of 83% of HIV-infected women by testing only 35% of women presenting to the clinic. Similarly, using the presenting complaint and various demographic and behavioral characteristics, it was possible to identify 84% of HIV-infected men by screening 40% of men presenting to the clinic. This study suggests that this method might provide a cost-effective approach that permits limited screening resources to be spent in a way that maximizes individual and societal benefit.

Adolescent↗

PubMed2004

Compliance of average- and intermediate-risk women to semiannual ovarian cancer screening.

OBJECTIVE: To report rates of compliance with an ovarian cancer screening protocol using serum CA125 and transvaginal sonography (TVS), performed semiannually on an alternating schedule, among participants at average or intermediate risk for developing ovarian cancer. METHODS: Two hundred ninety-two women at average or intermediate risk for developing ovarian cancer were randomly assigned to arms of a controlled clinical trial in which they received ovarian cancer screening consisting of serum CA125 alternating with TVS performed semiannually over 18 months, either alone or in combination with ovarian cancer risk education. A computerized tracking system generated screening appointment reminder letters and monitored adherence to scheduled screening. Participants overdue for scheduled screens received follow-up telephone calls consisting of up to four reminder messages left at 1-week intervals, and one to two interim attempts to reach participants between messages. The compliance rate for each screen was calculated as a ratio of the number of participants successfully completing the screen relative to the number expected to attend. Compliance rate by screen was: screen 1 (CA125) (97.3%), screen 2 (TVS) (82.5%), screen 3 (CA125) (79.0%), and screen 4 (TVS) (64.5%). One hundred seventy-two women completed all four screens and were classified as adherent to the screening protocol. Analysis by screening modality suggests that participants were more compliant to screens involving CA125. Age, educational background, distance from screening center, personal or family history of cancer, perceived risk of ovarian cancer, pre-enrollment ovarian cancer screening behavior, receiving an abnormal screen test result, and participation in ovarian cancer risk education sessions were not associated with adherence to the screening protocol or compliance to any of the screens. CONCLUSIONS: Despite extensive follow-up, compliance of average- and intermediate-risk women to an ovarian cancer screening protocol requiring semiannual screening diminishes rapidly. We propose that a semiannual ovarian cancer screening protocol, particularly one including TVS, may be too intensive for use in this population.

Adult↗

PubMed2003

Biomedical informatics for proteomics.

Success in proteomics depends upon careful study design and high-quality biological samples. Advanced information technologies, and also an ability to use existing knowledge to the full, will be crucial in making sense of the data. Despite its genome-scale potential, proteome analysis is at a much earlier stage of development than genomics and gene expression (microarray) studies. Fundamental issues involving biological variability, pre-analytic factors and analytical reproducibility remain to be resolved. Consequently, the analysis of proteomics data is currently informal and relies heavily on expert opinion. Databases and software tools developed for the analysis of molecular sequences and microarrays are helpful, but are limited owing to the unique attributes of proteomics data and differing research goals.

Biomedical Research↗

PubMed2003

Ovarian cancer screening.

In an ongoing effort to design an efficacious, cost-effective ovarian cancer screening method, the existing tests, CA 125 and transvaginal sonography, are being optimized and combined in a multimodal strategy, and new promising serum markers, such as mesothelin and HE4, are being developed and evaluated. Detection has been found to improve when multiple serum markers are used in a longitudinal logarithm. The parametric empirical Bayes approach improves screening algorithms by capturing the stability of markers over time in a heterogeneous population. It also has relatively simple extensions to multiple markers. The evaluation of markers increasingly accounts for characteristics of a woman that may affect her marker levels and accounts for the cancer's characteristics, histology, and grade. Receiver operating characteristic curves are helpful for evaluation because they relate a marker's sensitivity to the specificity at which it operates. Large, well-designed randomized controlled trials are under way to gauge the performance of existing screening methods.

Antigens, Tumor-Associated, Carbohydrate↗

PubMed2003

A parametric empirical Bayes method for cancer screening using longitudinal observations of a biomarker.

A revolution in molecular technology is leading to the discovery of many biomarkers of disease. Monitoring these biomarkers in a population may lead to earlier disease detection, and may prevent death from diseases like cancer that are more curable if found early. For markers whose concentration is associated with disease progression the earliest detection is achieved by monitoring the marker with an algorithm able to detect very small changes. One strategy is to monitor the biomarkers using a longitudinal algorithm that incorporates a subject's screening history into screening decisions. Longitudinal algorithms that have been proposed thus far rely on modeling the behavior of a biomarker from the moment of disease onset until its clinical presentation. Because the data needed to observe the early pre-clinical behavior of the biomarker may take years to accumulate, those algorithms are not appropriate for timely development using new biomarker discoveries. This manuscript presents a computationally simple longitudinal screening algorithm that can be implemented with data that is obtainable in a short period of time. For biomarkers meeting only a few modest assumptions our algorithm uniformly improves the sensitivity compared with simpler screening algorithms but maintains the same specificity. It is unclear what performance advantage more complex methods may have compared with our method, especially when there is doubt about the correct model for describing the behavior of the biomarker early in the disease process. Our method was specifically developed for use in screening for cancer with a new biomarker, but it is appropriate whenever the pre-clinical behavior of the disease and/or biomarker is uncertain.

Algorithms↗

PubMed2002

A quantitative assay for telomere protection in Saccharomyces cerevisiae.

Telomeres are the protective ends of linear chromosomes. Telomeric components have been identified and described by their abilities to bind telomeric DNA, affect telomere repeat length, participate in telomeric DNA replication, or modulate transcriptional silencing of telomere-adjacent genes; however, their roles in chromosome end protection are not as well defined. We have developed a genetic, quantitative assay in Saccharomyces cerevisiae to measure whether various telomeric components protect chromosome ends from homologous recombination. This "chromosomal cap" assay has revealed that the telomeric end-binding proteins, Cdc13p and Ku, both protect the chromosome end from homologous recombination, as does the ATM-related kinase, Tel1p. We propose that Cdc13p and Ku structurally inhibit recombination at telomeres and that Tel1p regulates the chromosomal cap, acting through Cdc13p. Analysis with recombination mutants indicated that telomeric homologous recombination events proceeded by different mechanisms, depending on which capping component was compromised. Furthermore, we found that neither telomere repeat length nor telomeric silencing correlated with chromosomal capping efficiency. This capping assay provides a sensitive in vivo approach for identifying the components of chromosome ends and the mechanisms by which they are protected.

Chromosomes, Fungal↗

PubMed2002

Combining several screening tests: optimality of the risk score.

The development of biomarkers for cancer screening is an active area of research. While several biomarkers exist, none is sufficiently sensitive and specific on its own for population screening. It is likely that successful screening programs will require combinations of multiple markers. We consider how to combine multiple disease markers for optimal performance of a screening program. We show that the risk score, defined as the probability of disease given data on multiple markers, is the optimal function in the sense that the receiver operating characteristic (ROC) curve is maximized at every point. Arguments draw on the Neyman-Pearson lemma. This contrasts with the corresponding optimality result of classic decision theory, which is set in a Bayesian framework and is based on minimizing an expected loss function associated with decision errors. Ours is an optimality result defined from a strictly frequentist point of view and does not rely on the notion of associating costs with misclassifications. The implication for data analysis is that binary regression methods can be used to yield appropriate relative weightings of different biomarkers, at least in large samples. We propose some modifications to standard binary regression methods for application to the disease screening problem. A flexible biologically motivated simulation model for cancer biomarkers is presented and we evaluate our methods by application to it. An application to real data concerning two ovarian cancer biomarkers is also presented. Our results are equally relevant to the more general medical diagnostic testing problem, where results of multiple tests or predictors are combined to yield a composite diagnostic test. Moreover, our methods justify the development of clinical prediction scores based on binary regression.

Bayes Theorem↗

PubMed2002

Generating longitudinal screening algorithms using novel biomarkers for disease.

Recent advances in molecular technology are leading to the discovery of new tumor biomarkers that may be useful for cancer screening and early diagnosis. Translating a potential screening biomarker from the laboratory to its use in patient care may require an algorithm or screening rule for its application. An algorithm that can detect the smallest deviation from a defined norm is likely to achieve the highest sensitivity, but any practical screening algorithm must do so with strict controls on test specificity to avoid false-positive results, and unnecessary patient alarm and risk. Longitudinal algorithms that make use of previous tumor marker values and trends are likely to obtain improvements over single threshold rules. Thus far, a few longitudinal screening algorithms have been proposed (e.g., using serial prostate-specific antigen values for the detection of prostate cancer and serial CA125 values for the detection of ovarian cancer), but these algorithms are not appropriate for novel tumor marker discoveries, because they rely on unverifiable assumptions that may not translate to the behavior of the new marker. The algorithm presented here is motivated by: (a) the need to develop an algorithm for early detection using novel markers; (b) the practical demands on data and specimen availability; and (c) the need to be robust enough to accommodate a wide range of tumor growth behavior. We use Parametric Empirical Bayes statistical theory to model the trajectory of markers over time in a cohort of asymptomatic healthy subjects, and use the estimated trajectory to produce person-specific thresholds that depend on the screening history of each person. The thresholds are chosen to give the person (or population) a specified false-positive rate. The resulting algorithm is simple and can be represented in a simple graph or a chart. The statistical analysis needed to generate the algorithm can be found in nearly every basic statistical package. The algorithm is highly robust and can detect a wide range of tumor behaviors. The Parametric Empirical Bayes screening algorithm should take a central role when evaluating marker discoveries for use in screening. The algorithm is particularly useful when screening with a new marker of which the behavior in the preclinical period is not well known.

Algorithms↗